Word2Vec Explained Simply: A Tutorial in Deep Learning (Tensorflow, Keras, & Python)

In this tutorial, we will be discussing Word2Vec, a popular technique for word embedding in natural language processing (NLP). Word2Vec is a shallow neural network model that is used to represent words in a continuous vector space. It has gained popularity for its ability to capture the semantic relationships between words, such as similarity and relatedness.

Word2Vec was developed by a team of researchers at Google in 2013 and has since become a widely used technique in NLP tasks such as sentiment analysis, language translation, and information retrieval. In this tutorial, we will discuss the two main variants of Word2Vec: Continuous Bag of Words (CBOW) and Skip-gram.

1. Continuous Bag of Words (CBOW):

CBOW is a model that aims to predict the center word in a window of context words. The input to the model is a set of context words, and the output is the target word. The model learns by minimizing the difference between the predicted word and the actual word using techniques like backpropagation.

The architecture of the CBOW model consists of an input layer, a hidden layer, and an output layer. The input layer receives the one-hot encoded representation of the context words, which is then passed through the hidden layer to generate a continuous vector representation. Finally, the output layer predicts the target word based on the vector representation.

2. Skip-gram:

Skip-gram is the inverse of CBOW, where the model aims to predict the context words given a target word. In this model, the input is a target word, and the output is a set of context words. The model learns to generate the context words by training on a large corpus of text data.

The architecture of the Skip-gram model is similar to CBOW, with an input layer, a hidden layer, and an output layer. The input layer receives the target word in one-hot encoded form, which is then passed through the hidden layer to generate a continuous vector representation. The output layer predicts the context words based on the vector representation.

To train a Word2Vec model, you will need a large corpus of text data, such as a collection of Wikipedia articles or news articles. You can use pre-trained Word2Vec models available online, such as the ones provided by Google or Stanford.

In this tutorial, we will be using TensorFlow and Keras to implement a Word2Vec model. First, we need to preprocess the text data by tokenizing the words and creating a vocabulary. Then, we can build the CBOW or Skip-gram model using the Sequential API in Keras.

Once the model is trained, we can use the trained embeddings to find similar words, calculate word similarities, or visualize the word embeddings using techniques like Principal Component Analysis (PCA) or t-SNE.

In conclusion, Word2Vec is a powerful technique for word embedding in NLP tasks. It can capture the semantic relationships between words by representing them in a continuous vector space. By training a Word2Vec model on a large corpus of text data, we can develop a better understanding of the semantic meaning of words and improve the performance of NLP tasks.